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dc.contributor.authorTarner, Hagenen_US
dc.contributor.authorBruder, Valentinen_US
dc.contributor.authorFrey, Steffenen_US
dc.contributor.authorErtl, Thomasen_US
dc.contributor.authorBeck, Fabianen_US
dc.contributor.editorBender, Janen_US
dc.contributor.editorBotsch, Marioen_US
dc.contributor.editorKeim, Daniel A.en_US
dc.date.accessioned2022-09-26T09:29:02Z
dc.date.available2022-09-26T09:29:02Z
dc.date.issued2022
dc.identifier.isbn978-3-03868-189-2
dc.identifier.urihttps://doi.org/10.2312/vmv.20221211
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20221211
dc.description.abstractEvaluation of rendering performance is crucial when selecting or developing algorithms, but challenging as performance can largely differ across a set of selected scenarios. Despite this, performance metrics are often reported and compared in a highly aggregated way. In this paper we suggest a more fine-grained approach for the evaluation of rendering performance, taking into account multiple perspectives on the scenario: camera position and orientation along different paths, rendering algorithms, image resolution, and hardware. The approach comprises a visual analysis system that shows and contrasts the data from these perspectives. The users can explore combinations of perspectives and gain insight into the performance characteristics of several rendering algorithms. A stylized representation of the camera path provides a base layout for arranging the multivariate performance data as radar charts, each comparing the same set of rendering algorithms while linking the performance data with the rendered images. To showcase our approach, we analyze two types of scientific visualization benchmarks.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleVisually Comparing Rendering Performance from Multiple Perspectivesen_US
dc.description.seriesinformationVision, Modeling, and Visualization
dc.description.sectionheadersSession V
dc.identifier.doi10.2312/vmv.20221211
dc.identifier.pages115-125
dc.identifier.pages11 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License